Articles tagged with Artificial Neural Networks
A new system called EasySort AUTO uses artificial intelligence image recognition to sort single bacterial cells, increasing efficiency by over 93% and preserving cell vitality. The technology has been successfully tested on yeast and E. coli bacteria, with over 80% of sorted cells able to be cultured.
Researchers from Brown and MIT developed a new framework that uses machine learning and sequential sampling to predict rare disasters like earthquakes and pandemics with less data. The framework, called DeepOnet, has been shown to outperform traditional modeling efforts in predicting scenarios, probabilities and timelines of rare events.
Researchers developed an AI-based neural network to detect early knee osteoarthritis from x-ray images, matching doctors' diagnoses in 87% of cases. This method could help reduce unnecessary examinations, treatments, and even knee joint replacement surgery.
A team of researchers from the University of Pennsylvania has developed a new algorithm, metadynamics, that can navigate high-dimensional energy landscapes to find low-energy configurations. This breakthrough has the potential to revolutionize fields such as protein folding and machine learning.
A neural network trained using a diverse dataset outperforms conventionally trained algorithms by reducing bias in artificial intelligence. The use of images from low-resource populations boosts the object recognition performance of machine learning systems.
Engineers at Tokyo Tech demonstrated a simple approach to improve AI classifier training using limited sensor data, increasing quality without extra cost. The proposed method promises to address the challenge of classification accuracy in real-world applications, where reliable answers are crucial.
Researchers from the University of Johannesburg deployed Few Shot Learning (FSL) for NIALM, a non-intrusive appliance load monitoring system. FSL requires only 7 test images to recognize appliances with 97.83% accuracy, making it faster and more cost-effective than traditional Machine Learning.
Researchers found that artificial neural networks learn better with occasional periods of inactivity, similar to the human brain's benefits of sleep. This approach mitigates catastrophic forgetting and allows for continuous learning, like humans or animals.
Researchers created synthetic knee x-ray images to complement real images in osteoarthritis classification. Medical experts were unable to distinguish between authentic and synthetic images, highlighting the potential of synthetic data for collaboration and testing.
Researchers induced brain-like sleep in an artificial spiking neural network, enabling it to retain previously learned information. This breakthrough helps the model avoid catastrophic forgetting and improve its overall performance over time.
The AlphaFold2 AI model has contributed 25% more high-quality protein structures to existing species, aiding in understanding protein function and designing targeted drugs for cancer. Despite limitations, its impact will transform life sciences with new computational tools.
Researchers trained a mouse brain model to solve visual tasks, achieving superior robustness and performance compared to traditional neural networks. The model's unique coding properties enable it to cope with errors and unexpected input, making it a promising tool for advances in neuromorphic computing.
Deep learning models can become less accurate in recognizing specific categories of images, sounds, or text after network pruning. Researchers demonstrate a technique to address this challenge, improving the fairness of deep learning models.
Researchers at WVU are developing software for robots to learn and adapt in real-time, inspired by the neural networks of electric fish. The goal is to enable robots to navigate different terrains autonomously without human supervision.
Researchers from University of Warsaw create spiking neuron using photons to mimic biological brain's behavior. This achievement paves the way for photonic neural networks that process information faster and more efficiently than conventional systems.
Researchers have developed a deep learning algorithm that can accurately assess the stage of head and neck cancer using standard CT scans, outperforming expert radiologists. The algorithm demonstrated superior accuracy in measuring the extent of cancer spread, especially for patients with high-risk disease.
Researchers at MIT have developed a new method that uses optics to accelerate machine-learning computations on low-power devices. By encoding model components onto light waves, data can be transmitted rapidly and computations performed quickly, leading to over a hundredfold improvement in energy efficiency.
Researchers develop mechanical neural networks (MNNs) with tunable beams that can learn behaviors and adapt to external forces. The MNNs, composed of a triangular lattice pattern, exhibit smart properties through machine learning algorithms. Early prototypes overcame lag issues and achieved accurate performance in various applications.
A new CNN framework, PE-Net, is proposed for predicting machine remaining useful life (RUL) accurately. The framework uses a novel architecture with small-sized one-dimensional convolution kernels and deep networks to learn features from input time series signals.
Scientists have developed a solution to communication challenges in neuromorphic chips using superconducting devices. This allows artificial neural systems to operate 100,000 times faster than the human brain, with potential applications in industrial control and human conversations.
A team of researchers at Harvard University has developed an ionic circuit that performs analog matrix multiplication, a key operation in neural networks, using ions in liquid. The breakthrough uses a pH-gated ionic transistor and expands to a 16x16 array for more complex computations.
A recent grant will fund a project developing new hardware for machine learning, aiming to curb unsustainable energy use in AI systems. The new algorithms being developed are made available to the research community and compatible with an openly shared computing platform.
A recent study published in Aging-US found that feeling lonely, unhappy, or hopeless increases one's biological age more than smoking. The research used digital models of aging to analyze the effects of various factors on aging rates, revealing a significant correlation between mental health and accelerated aging.
Current AI models are restricted by a lack of experience in real-world environments, despite achieving significant advancements in virtual settings. Researchers are now exploring ways to bridge this gap with foundation models that can operate in physical spaces.
City digital twin technology is used to create synthetic training data for deep learning models, which are then trained on a combination of real and synthetic data. This approach yields promising results for architectural segmentation tasks, particularly for modern building styles.
A team led by York University has developed a new technique to keep drinking water safe in refugee settlements using machine learning and ensemble forecasting systems. The approach can predict the probability of residual chlorine remaining in stored water, providing critical information for aid workers to ensure safe drinking water.
Rice University's ROBE Array algorithm slashes the size of DLRM memory structures, allowing training on 100 megabytes of memory and a single GPU. The method matches state-of-the-art DLRM training methods with improved inference efficiency.
Researchers at MIT developed an AI model that can detect Parkinson's disease from breathing patterns, using a neural network to assess the presence and severity of the condition. The device is non-invasive and can be used in patients' homes without any bodily contact.
The NeuRRAM chip demonstrates wide range of AI applications with equivalent accuracy while reducing energy consumption by up to 70% compared to traditional compute platforms. It also supports various neural network models and architectures, enabling diverse AI applications on edge devices.
The study demonstrates the creation of physical reservoirs using chaotic dynamics, enabling alternative approach to AI-based pattern detection. The researchers exploited emergence and pattern formation phenomena under incomplete synchronization in chaotic dynamics, revealing a rich variety of ways in which the network synchronizes.
Researchers developed a Flashover Prediction Neural Network (FlashNet) model to forecast deadly fire events, beating other AI-based tools with up to 92.1% accuracy across various building floorplans. The model's performance improved when given real-world data, highlighting its potential for saving firefighter lives.
Researchers successfully taught microrobots to swim via deep reinforcement learning, allowing them to adapt to changing conditions and perform complex maneuvers. The AI-powered swimmers can navigate toward any target location on their own, showcasing their robust performance in fluid flows and uncontrolled environments.
Researchers at MIT have developed a machine-learning system that uses computer vision to monitor the 3D printing process and correct errors in real-time. The system successfully printed objects more accurately than other 3D printing controllers, enabling engineers to incorporate novel materials into their prints with ease.
The new AI system uses associative learning to detect similarities in datasets, reducing processing time and computational cost. By leveraging optical parallel processing and light signals, the system can identify patterns and associations more efficiently than conventional machine learning algorithms.
Artificial neural networks use white matter cracks and cerebral cortex furrows to estimate biological age, which can indicate possible disease or injury. The 'black box' problem has been solved, revealing the algorithm's decision-making process.
Researchers at UVA have created a new AI tool called SITHCon that can decode speech in different speeds, enabling machines to process information more efficiently. This breakthrough technology has the potential to alter how artificial neural networks 'think' and reduce AI's massive carbon footprint.
Researchers at NIST have developed a new type of hardware for AI that uses magnetic tunnel junctions, which are less energy-intensive than traditional silicon chips. The new technology has already passed a virtual wine-tasting test and shows promise for reducing energy use in AI systems.
Researchers from Osaka University found that facial similarity plays a crucial role in ratings of trustworthiness for observers of the same sex, but not for observers of opposite sex. The study suggests that facial similarity is an important factor affecting social judgments for same-sex interactions.
Researchers developed a novel convolutional neural network for facial expression recognition, outperforming conventional models while being computationally less expensive. The new model achieved an accuracy of 72.4% using only 58,000 parameters.
A team of researchers led by Danilo Vasconcellos Vargas has developed a new method called 'Raw Zero-Shot' to evaluate the robustness of artificial neural networks in image recognition. The study found that Capsule Networks produced the densest clusters, indicating improved transferability and potential solutions for improving AI robust...
Researchers from the University of Tsukuba develop a model that combines multiple theories to simulate motor learning in humans. The study found that larger amounts of motor exploration aid in learning sensitivity derivatives and transforming errors into motor corrections.
A new AI system uses artificial neural networks to recognize objects more accurately and stably, despite changing visual inputs. The system mimics human eye movements to improve machine vision capabilities, reducing errors in self-driving cars and other applications.
A new AI-powered mental health application, FuturSelf, uses machine learning to identify the shortest path to mental stability. The system offers personalized recommendations for improving long-term well-being.
Researchers use machine learning to automatically analyze Reflection High-Energy Electron Diffraction (RHEED) data, enabling faster and more efficient discovery of new materials. The study focused on surface superstructures in thin-film silicon surfaces and identified optimal synthesis conditions using non-negative matrix factorization.
Researchers have developed an AI-powered approach to calculate molecular spectra using Graph Neural Networks (GNNs), significantly reducing computation time and improving accuracy. The SchNet model achieved a 20% increase in accuracy while reducing computational time, enabling the analysis of complex molecules like quantum dots.
The Department of Energy has awarded Early Career Research Program funding to three Oak Ridge National Laboratory scientists. The awardees will receive $500,000 annually for five years to support their research in fusion energy, advanced scientific computing, and biogeochemical controls on phosphorus cycling.
A new training algorithm for deep spiking neural networks (SNNs) uses biologically plausible spatiotemporal adjustment to improve performance and reduce energy consumption. This approach achieves competitive classification accuracy with only 3% of the energy used by traditional artificial neural networks.
A team from Nagoya University created an artificial neural network model that performed the delayed matching-to-sample task and analyzed its behavior. The model was able to evolve to exhibit human-like metamemory, adapting to its environment by learning and evolving. This breakthrough aims to create machines with memories like humans.
A study by HBP scientists found that wakefulness, non-REM sleep, and REM sleep have complementary functions for learning: experiencing stimuli, solidifying experiences, and discovering semantic concepts. This research suggests that unusual dreams, simulated using Generative Adversarial Networks, can improve brain learning by introducin...
The university's new Robotics and Autonomous Systems Teaching and Innovation Center (RASTIC) will provide students with hands-on experience in robotics, autonomous systems, and self-driving technology. The lab aims to boost Massachusetts' competitiveness in the tech sector by supporting innovative projects and startups.
Researchers at the University of Missouri are applying AI to analyze protein dynamics, identifying potential target sites for new drug therapies. The approach can simulate protein changes related to conditions like cancer, enhancing the chances of successful therapies.
MIT researchers develop ExSum, a framework to formalize explanations of machine-learning models into quantifiable rules. This allows for testing assumptions about model behavior and reveals unexpected insights, such as negative words having sharper contributions to model decisions.
Researchers have developed a method using nanomagnets to perform artificial intelligence, slashing energy costs and offering huge efficiency gains. The technology uses 'nanomagnetic states' to process and store data, cutting out the need for software simulation.
Artificial Intelligence can now identify legendary batting techniques used by Sir Donald Bradman and modern players. Researchers developed a deep learning computer vision AI model to detect lateral backlift batters from straight ones.
Scientists at the University of Oxford have developed an 'optomemristor' device that facilitates three-factor learning and emulation of biological computations, making it possible to perform complex machine learning tasks. The device uses both light and electrical signals to interact and consume very little energy.
A study found that trainee teachers who received AI-generated feedback improved their diagnostic reasoning, identifying potential learning difficulties in pupils more accurately. The AI system analyzed the trainees' work and provided clear, adaptive feedback.
A new AI-based approach can predict cardiac arrest with significant accuracy, identifying patients at risk and predicting the likelihood of sudden cardiac death. The technology stands to transform clinical decision-making and increase survival rates from lethal arrhythmias.
Researchers have developed a new method called Shared Interest that enables users to aggregate, sort, and rank individual explanations of a machine-learning model's reasoning. This technique uses quantifiable metrics to compare how well the model's reasoning matches human thinking, helping to uncover concerning trends in decision-making.
Researchers found that AI-enhanced diagnosis helps doctors accurately detect fetal congenital heart disease, with fellows making the most accurate diagnoses. The new system uses graphical charts to represent the AI's analysis of ultrasound videos, improving accuracy and trust among medical professionals.
Researchers at MIT developed a framework for robotic manipulation systems that can perform complex tasks using a two-stage learning process. This allows robots to learn abstract ideas about manipulating deformable objects, such as pizza dough, and execute skills to complete tasks.